Machine Learning : What is Supervised Learning

We discussed Machine learning in my previous post. Now let’s discuss about Supervised Learning; the most common and widely used branch of Machine Learning.

Suppose you are a student who wants to learn a new language, you will need a teacher who can teach you different words of that language and their meaning. Once you understand and memorize these words; you can form a sentence and finally able to speak and write in that language. This entire learning occurred under the Supervision of that teacher, so you can say that it was a Supervised learning.

Similarly, in Supervised Machine learning, set of instances are provided to the learning Algorithm, each instance contains an input and an output. The Algorithm learns from these instances and builds a model which can then find output to any new input.

 In Supervised learning, you show the machine the connection between different variables and known outcomes. This is called Labeled data and Sample output. Therefore, you can also say that Supervised learning is Learning with Labeled Data.

Suppose you want to build a program which can identify different types of vehicles on the road, In order to train the algorithm you need to feed images of different vehicles to it and assign labels to those images, for example you upload images of different cars and tag them as Cars. This process is called Training the algorithm, after training completes, the time for Testing the Algorithm arrives; during Testing; we upload images of a random vehicle without assigning any tags to them. The algorithm then analyze each image and assign a tag to it, if the tag is correct then training has been successful and the Machine has learned.

There are many algorithms used in Supervised Learning, they can be broadly categorized into two categories:

1. Classification
2. Regression

Thank you for reading this blog and happy learning.

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